preprint · arXiv (Cornell University) · 2019

A Python Library For Empirical Calibration

Xiaojing Wang, Jingang Miao, Yunting Sun

Cited by 0Open access

Abstract

Dealing with biased data samples is a common task across many statistical fields. In survey sampling, bias often occurs due to unrepresentative samples. In causal studies with observational data, the treated versus untreated group assignment is often correlated with covariates, i.e., not random. Empirical calibration is a generic weighting method that presents a unified view on correcting or reducing the data biases for the tasks mentioned above. We provide a Python library EC to compute the empirical calibration weights. The problem is formulated as convex optimization and solved efficiently in the dual form. Compared to existing software, EC is both more efficient and robust. EC also accommodates different optimization objectives, supports weight clipping, and allows inexact calibration, which improves usability. We demonstrate its usage across various experiments with both simulated and real-world data.

Cite this paper

APA 7

Wang, X., Miao, J., & Sun, Y. (2019). A Python Library For Empirical Calibration. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1906.11920

MLA 9

Wang, Xiaojing, et al. “A Python Library For Empirical Calibration.” arXiv (Cornell University), 2019, https://doi.org/10.48550/arxiv.1906.11920.

Chicago

Wang, Xiaojing, Jingang Miao, and Yunting Sun. 2019. “A Python Library For Empirical Calibration.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1906.11920.

Harvard

Wang, X., Miao, J. and Sun, Y. (2019) ‘A Python Library For Empirical Calibration’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1906.11920.

BibTeX

@misc{wang2019python,
  title = {{A Python Library For Empirical Calibration}},
  author = {Wang, Xiaojing and Miao, Jingang and Sun, Yunting},
  journal = {arXiv (Cornell University)},
  year = {2019},
  doi = {10.48550/arxiv.1906.11920}
}

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